Nature Methods: 统计方法科普系列（Points of Significance）

Nature网站上面向生物学家的统计教学文章汇总（Statistics for biologists）：http://www.nature.com/collections/qghhqm/转自上面第一个网页：Importance of being uncertain – September 2013How samples are used to estimate population statistics and what this means in terms of uncertainty.Error Bars – October 2013The use of error bars to represent uncertainty and advice on how to interpret them.Significance, P values and t-tests – November 2013Introduction to the concept of statistical significance and the one-sample t-test.Power and sample size – December 2013Using statistical power to optimize study design and sample numbers.Visualizing samples with box plots – February 2014Introduction to box plots and their use to illustrate the spread and differences of samples.Comparing samples—part I – March 2014How to use the two-sample t-test to compare either uncorrelated or correlated samples.Comparing samples—part II – April 2014Adjustment and reinterpretation of P values when large numbers of tests are performed.Nonparametric tests – May 2014Use of nonparametric tests to robustly compare skewed or ranked data.Designing comparative experiments – June 2014The first of a series of columns that tackle experimental design shows how a paired design achieves sensitivity and specificity requirements despite biological and technical variability.Analysis of variance and blocking – July 2014Introduction to ANOVA and the importance of blocking in good experimental design to mitigate experimental error and the impact of factors not under study.Replication – September 2014Technical replication reveals technical variation while biological replication is required for biological inference.Nested designs – October 2014Use the relative noise contribution of each layer in nested experimental designs to optimally allocate experimental resources using ANOVA.Two-factor designs – December 2014It is common in biological systems for multiple experimental factors to produce interacting effects on a system. A study design that allows these interactions can increase sensitivity.Sources of variation – January 2015To generalize experimental conclusions to a population, it is critical to sample its variation while using experimental control, randomization, blocking and replication to collect replicable and meaningful results.Split plot design – March 2015When some experimental factors are harder to vary than others, a split plot design can be efficient for exploring the main (average) effects and interactions of the factors.Bayes’ theorem – April 2015Use Bayes’ theorem to combine prior knowledge with observations of a system and make predictions about it.Bayesian statistics – May 2015Unlike classical frequentist statistics, Bayesian statistics allows direct inference of the probability that a model is correct and it provides the ability to update this probability as new data is collected.Sampling distributions and the bootstrap – June 2015Use the bootstrap method to simulate new samples and assess the precision and bias of sample estimates.Bayesian networks – September 2015Model interactions between causes and effects in large networks of causal influences using Bayesian networks, which combine network analysis with Bayesian statistics.Association, correlation and causation – October 2015Pairwise dependencies can be characterized using correlation but be aware that correlation only implies association, not causation. Conversely, causation implies association, not correlation.Simple linear regression – November 2015Linear regression is a flexible way to predict the values of one variable using the values of the other to find a ‘best line’ through the data points.